{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tutorial notebook 7: Custom prompt templates\n",
    "\n",
    "In this tutorial, we will show how to use custom prompt templates for finetuning Cell2Sentence models on your own tasks and datasets. This may be needed if you want to use Cell2Sentence for a task that is not one of the standard tasks supported by Cell2Sentence.\n",
    "\n",
    "At a high level, we will:\n",
    "1. Write a custom prompt template\n",
    "2. Subclass the PromptFormatter class to implement a function that formats each cell sentence sample in our dataset with our custom prompt template.\n",
    "3. Finetune a Cell2Sentence model on our custom formatted dataset.\n",
    "\n",
    "We will use the same dataset and model as in the previous tutorials."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python built-in libraries\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\" \n",
    "os.environ[\"WORLD_SIZE\"] = \"1\"\n",
    "\n",
    "import pickle\n",
    "import random\n",
    "from datetime import datetime\n",
    "from collections import Counter\n",
    "\n",
    "# Third-party libraries\n",
    "from datasets import Dataset\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from transformers import TrainingArguments\n",
    "\n",
    "# Single-cell libraries\n",
    "import anndata\n",
    "import scanpy as sc\n",
    "\n",
    "# Cell2Sentence imports\n",
    "import cell2sentence as cs\n",
    "from cell2sentence.prompt_formatter import get_cell_sentence_str\n",
    "from cell2sentence.tasks import predict_cell_types_of_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cell2sentence.prompt_formatter import PromptFormatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "SEED = 1234\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Data\n",
    "\n",
    "Next, we will load the preprocessed dataset from the tutorial 0. This dataset has already been filtered and normalized, so it it ready for transformation into cell sentences.\n",
    "\n",
    "<font color='red'>Please make sure you have completed the preprocessing steps in Tutorial 0 before running the following code, if you are using your own dataset.</font>. Ensure that the file path is correctly set in <font color='gold'>DATA_PATH</font> to where your preprocessed data was saved from tutorial 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_PATH = \"/home/sr2464/scratch/C2S_Files/Cell2Sentence_Datasets/dominguez_conde_immune_tissue_two_donors_preprocessed_tutorial_0.h5ad\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 29773 × 23944\n",
       "    obs: 'cell_type', 'tissue', 'batch_condition', 'organism', 'assay', 'sex', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt'\n",
       "    var: 'gene_name', 'ensembl_id', 'n_cells', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'\n",
       "    uns: 'batch_condition_colors', 'cell_type_colors', 'log1p', 'neighbors', 'pca', 'tissue_colors', 'umap'\n",
       "    obsm: 'X_pca', 'X_umap'\n",
       "    varm: 'PCs'\n",
       "    obsp: 'connectivities', 'distances'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata = anndata.read_h5ad(DATA_PATH)\n",
    "adata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata.obs = adata.obs[[\"cell_type\", \"tissue\", \"batch_condition\", \"organism\", \"sex\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell_type</th>\n",
       "      <th>tissue</th>\n",
       "      <th>batch_condition</th>\n",
       "      <th>organism</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pan_T7935490_AAACCTGCAAATTGCC</th>\n",
       "      <td>CD4-positive helper T cell</td>\n",
       "      <td>ileum</td>\n",
       "      <td>A29</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pan_T7935490_AAACGGGCATCTGGTA</th>\n",
       "      <td>CD8-positive, alpha-beta memory T cell</td>\n",
       "      <td>ileum</td>\n",
       "      <td>A29</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pan_T7935490_AAACGGGTCTTGCATT</th>\n",
       "      <td>CD8-positive, alpha-beta memory T cell</td>\n",
       "      <td>ileum</td>\n",
       "      <td>A29</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pan_T7935490_AAAGCAATCATCGCTC</th>\n",
       "      <td>CD8-positive, alpha-beta memory T cell</td>\n",
       "      <td>ileum</td>\n",
       "      <td>A29</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pan_T7935490_AAAGTAGCAGTCACTA</th>\n",
       "      <td>gamma-delta T cell</td>\n",
       "      <td>ileum</td>\n",
       "      <td>A29</td>\n",
       "      <td>Homo sapiens</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                            cell_type tissue  \\\n",
       "Pan_T7935490_AAACCTGCAAATTGCC              CD4-positive helper T cell  ileum   \n",
       "Pan_T7935490_AAACGGGCATCTGGTA  CD8-positive, alpha-beta memory T cell  ileum   \n",
       "Pan_T7935490_AAACGGGTCTTGCATT  CD8-positive, alpha-beta memory T cell  ileum   \n",
       "Pan_T7935490_AAAGCAATCATCGCTC  CD8-positive, alpha-beta memory T cell  ileum   \n",
       "Pan_T7935490_AAAGTAGCAGTCACTA                      gamma-delta T cell  ileum   \n",
       "\n",
       "                              batch_condition      organism     sex  \n",
       "Pan_T7935490_AAACCTGCAAATTGCC             A29  Homo sapiens  female  \n",
       "Pan_T7935490_AAACGGGCATCTGGTA             A29  Homo sapiens  female  \n",
       "Pan_T7935490_AAACGGGTCTTGCATT             A29  Homo sapiens  female  \n",
       "Pan_T7935490_AAAGCAATCATCGCTC             A29  Homo sapiens  female  \n",
       "Pan_T7935490_AAAGTAGCAGTCACTA             A29  Homo sapiens  female  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene_name</th>\n",
       "      <th>ensembl_id</th>\n",
       "      <th>n_cells</th>\n",
       "      <th>mt</th>\n",
       "      <th>n_cells_by_counts</th>\n",
       "      <th>mean_counts</th>\n",
       "      <th>pct_dropout_by_counts</th>\n",
       "      <th>total_counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RP11-34P13</th>\n",
       "      <td>RP11-34P13</td>\n",
       "      <td>ENSG00000238009</td>\n",
       "      <td>38</td>\n",
       "      <td>False</td>\n",
       "      <td>38</td>\n",
       "      <td>0.001310</td>\n",
       "      <td>99.872368</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RP11-34P13-3</th>\n",
       "      <td>RP11-34P13</td>\n",
       "      <td>ENSG00000241860</td>\n",
       "      <td>106</td>\n",
       "      <td>False</td>\n",
       "      <td>106</td>\n",
       "      <td>0.003627</td>\n",
       "      <td>99.643973</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AP006222</th>\n",
       "      <td>AP006222</td>\n",
       "      <td>ENSG00000286448</td>\n",
       "      <td>7</td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>99.976489</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LINC01409</th>\n",
       "      <td>LINC01409</td>\n",
       "      <td>ENSG00000237491</td>\n",
       "      <td>1292</td>\n",
       "      <td>False</td>\n",
       "      <td>1292</td>\n",
       "      <td>0.045981</td>\n",
       "      <td>95.660498</td>\n",
       "      <td>1369.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAM87B</th>\n",
       "      <td>FAM87B</td>\n",
       "      <td>ENSG00000177757</td>\n",
       "      <td>3</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>99.989924</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               gene_name       ensembl_id  n_cells     mt  n_cells_by_counts  \\\n",
       "RP11-34P13    RP11-34P13  ENSG00000238009       38  False                 38   \n",
       "RP11-34P13-3  RP11-34P13  ENSG00000241860      106  False                106   \n",
       "AP006222        AP006222  ENSG00000286448        7  False                  7   \n",
       "LINC01409      LINC01409  ENSG00000237491     1292  False               1292   \n",
       "FAM87B            FAM87B  ENSG00000177757        3  False                  3   \n",
       "\n",
       "              mean_counts  pct_dropout_by_counts  total_counts  \n",
       "RP11-34P13       0.001310              99.872368          39.0  \n",
       "RP11-34P13-3     0.003627              99.643973         108.0  \n",
       "AP006222         0.000235              99.976489           7.0  \n",
       "LINC01409        0.045981              95.660498        1369.0  \n",
       "FAM87B           0.000101              99.989924           3.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.var.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/gpfs/radev/home/ap2853/.conda/envs/scFB2/lib/python3.8/site-packages/scanpy/plotting/_tools/scatterplots.py:394: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sc.pl.umap(\n",
    "    adata,\n",
    "    color=\"cell_type\",\n",
    "    size=8,\n",
    "    title=\"Human Immune Tissue UMAP\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.408124"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.X.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are expecting log10 base 10 transformed data, with a maximum value somewhere around 3 or 4. Make sure to start with processed and normalized data when doing the cell sentence conversion!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cell2Sentence Conversion\n",
    "\n",
    "In this section, we will transform our AnnData object containing our single-cell dataset into a Cell2Sentence (C2S) dataset by calling the functions of the CSData class in the C2S code base. Full documentation for the functions of the CSData class can be found in the documentation page of C2S."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cell_type', 'tissue', 'batch_condition', 'organism', 'sex']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata_obs_cols_to_keep = adata.obs.columns.tolist()\n",
    "adata_obs_cols_to_keep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 29773/29773 [00:09<00:00, 3150.71it/s]\n"
     ]
    }
   ],
   "source": [
    "# Create CSData object\n",
    "arrow_ds, vocabulary = cs.CSData.adata_to_arrow(\n",
    "    adata=adata, \n",
    "    random_state=SEED, \n",
    "    sentence_delimiter=' ',\n",
    "    label_col_names=adata_obs_cols_to_keep\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['cell_name', 'cell_sentence', 'cell_type', 'tissue', 'batch_condition', 'organism', 'sex'],\n",
       "    num_rows: 29773\n",
       "})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrow_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'cell_name': 'Pan_T7935490_AAACCTGCAAATTGCC',\n",
       " 'cell_sentence': 'RPLP1 ACTB EEF1A1 HSP90AA1 TMSB4X B2M FTH1 KLF6 HSPA1B MALAT1 RPS12 HSPA8 RPL13 MT-CO1 ATF3 MT-CO2 RPL41 TPT1 MT-CO3 RPS19 HLA-B RPL10 RPS4X RPL28 MT-CYB DUSP1 RPL30 MT-ND4L RPS15 FOS RPL34 RPS2 RPLP2 MT-ND3 RPS18 RPS8 TRBV7-2 RPL32 RPS3 ANXA1 RPL11 HLA-C RPS27 ACTG1 UBC RPL3 RPL37 RPLP0 MT-ATP6 JUNB RPS28 RPL18 UBB MT-ATP8 RPS14 RPL39 PFN1 GAPDH HSPA1A RPL18A SRGN RPS27A RPL26 RPL19 RPS15A HLA-A DNAJB1 RPS3A CREM RPS13 MT-ND1 RPL21 RPS25 BTG2 RPL35A FAU RPL8 RPL7A RPS24 RPS6 RPS16 RACK1 NFKBIA RGS1 RPL29 CALM1 RPL9 RPL37A MT-ND5 TNFAIP3 RPS23 IL7R RPL36A PTMA NFKBIZ UBA52 EIF1 CRIP1 CORO1A RPL14 HSP90AB1 RPL10A CXCR4 RPL4 EEF1B2 RPL36 RPS9 RPL27 NACA VIM H3-3B RPS7 HSPH1 ATP5F1E HLA-E RPL17 RPSA MYL12A RPL12 CD69 TAGAP RPL35 RPS29 RPL6 SARAF ZFP36L2 MT-ND4 ARHGDIB BTG1 RPS21 EEF1D PNRC1 EEF1G HSPA5 FYB1 CD3E IFITM1 RNASEK EEF2 MT-ND2 FTL S100A4 JUN IFITM2 CYTIP OST4 LAPTM5 RPL36AL PLAAT4 PFDN5 SAMSN1 DNAJA1 EIF4A1 FXYD5 HSPE1 CTLA4 IL32 RPL24 CFL1 SAT1 RPL13A NPM1 NDUFV2 SRSF7 ITM2B POLR1D CCNI CALR ZFP36 COX7C PTPRC GADD45A CD3G RAC2 MYL6 UBE2D3 CCL20 RPS5 RPL22 TOMM7 RPL15 C12ORF57 LSP1 SON H4C3 RPL23A RPL31 DDX5 EVL AHR SERF2 STK17A CD6 PTGER4 OSTF1 OAZ1 TMA7 PHLDA1 COMMD6 PPIA TMSB10 RHOH TUBA1B KTN1 PDIA3 DUSP5 BTF3 SSR4 ATP5MC2 S100A6 CEBPD TRAV13-2 STAT1 HSPB1 PSMB9 RPL38 HNRNPA2B1 FLT3LG CSTB CDK2AP2 CSK MIF MCL1 PSME1 TXNIP RHOA HNRNPA1 UBL5 PTGES3 PDCD4 SLC2A3 ERCC1 SNHG29 NFKBID SRP14 ARPC1B NR4A2 CMTM6 CD44 ITGA4 GNB2 TRAV19 BHLHE40 HMGB1 ARPC3 TRIR PRKCH NME2 KLRB1 CLIC1 ANAPC16 NCOR1 COX5B MYL12B SAP18 RHOB TCF25 SOD1 SRSF9 FOSB PCBP1 CD96 TBC1D10C CD164 CD3D ACTR2 COX4I1 SH3BGRL3 KMT2E HSPD1 PABPC1 SPCS2 SERP1 TNFRSF1B TAGLN2 PCBP2 RPL5 LDLR YWHAB CSRNP1 COX8A MYADM TRMT112 ATP5MG GABARAP ELF1 PRR13 RPL22L1 WAKMAR2 SIT1 GADD45B RPS17 S100A10 RBBP4 HNRNPDL CD2 UQCRQ COX6B1 RGS2 CHCHD2 TAF1B RPL27A TAP1 MTDH CD99 ARF1 TBCA TMEM14B HNRNPA0 RPS11 SEC61A1 RSRP1 TUBB ACIN1 HNRNPUL1 OXNAD1 LMO4 CTSH RBM8A RGL4 NFU1 AP1G2 TSC22D3 CASS4 CCNH RPS26 PIK3R1 SH3KBP1 SSBP4 TPR GADD45G ZFAS1 COTL1 BCL7C MZT2A LAT FUS SDF2L1 KRTCAP2 MRPL41 CENPX SETD5 TMC8 COX17 DNAJB4 CDKN1B ODC1 ACAP1 ANXA6 CYBA SUMO3 TMBIM6 TMEM259 CD37 PRDX2 UBE2D2 BTG3 PARK7 CD247 TMEM258 HNRNPAB SPOCK2 SEPTIN1 LAMTOR5 RNF11 SNX5 SASH3 PPM1G ARF6 EIF5A IL4I1 RBM3 RSF1 SLA CALM3 SNRPB SHISA5 ARID5B KXD1 PTPRCAP GBP1 STN1 NSA2 ATP1A1 LCK PSMB8 PSMB3 HNRNPC RNF167 RESF1 ACTR3 CKLF CD52 RPS27L GSTK1 GIMAP7 DCXR RGS14 VASP PBXIP1 GOLGA4 RPL23 YWHAQ SNHG16 TAOK3 IGBP1 DOCK10 SRRM1 PSMC3 IKZF3 AHI1 RPL7 TMEM87A EIF5 MED1 ANKRD12 IWS1 SLC25A6 TUBA1C LRRC41 SEC11C DLST VMA21 MKNK2 RALB THUMPD1 DBF4 RHEB WASHC3 VRK2 SUPT4H1 FADD CHD1 CAPN1 TMEM179B CKS2 RGS10 EIF4E BPTF RNMT DDX39A GADD45GIP1 DYNLL1 SF3B2 CDKN2AIP RAP1B ACTN4 ABRACL MOB4 METTL23 ITSN2 MTF2 VSIR PER1 COX7A2 COX14 IPCEF1 YWHAH CYRIB MOB3A SP2 BCL11B STUB1 RNF213 PRKRA EDF1 PRPF38B RHOG BAG1 ATOX1 CACYBP MAP4K1 PPP1R2 PAXX ADA NDUFA12 WAC ELK3 PRRC2C MUS81 CCT4 ARL5B RALBP1 FBXW11 LCP1 LEPROTL1 PDCD10 HNRNPR NR3C1 PPP4C BATF ABLIM1 PSMA2 TRAT1 RUNX3 CIB1 ARPC2 ABI3 UGP2 NDUFAB1 HCLS1 DERL1 RAB5A INSIG1 CNBP CHROMR DSTYK DPP4 TOMM6 PDE4D LUC7L2 JUND ARHGDIA CEMIP2 POLE4 ANXA11 PLEKHB2 SDCBP MANF FAM83D IMP3 TMCO1 SUCLG1 MBD2 NDUFA10 PDIA6 S100A11 DNPH1 TARDBP PTDSS1 RP11-25K19 LPXN FYTTD1 RORA NASP ATP6V1F RIPK2 IDI1 RASSF7 MT-ND6 RWDD1 CYB5R3 MAZ PIM1 PSMA3 GPX4 PACS1 TNIP1 PLEKHF1 STXBP2 MSMO1 UQCR11 EPB41L4A-AS1 TMIGD2 PATL1 COX6A1 CCND3 TUBB4B OCIAD2 UBE2V2 ASMTL MAP3K8 FERMT3 SH2B1 ACP5 RPS6KB2 GABPB1-AS1 MTRNR2L12 ZFAND2A EIF3D ATP5PB NEDD8 NDUFA1 C19ORF25 CYB561D2_ENSG00000114395 PPP1R18 SYF2 PIGT SLC25A3 KHDRBS1 GIMAP4 NAXE HMGN1 RGS19 SUMO1 YWHAZ ERN1 LRRFIP1 NCF1 GALNT11 PSMG2 SNRPA1 IFNGR1 ENSA LAMTOR4 TBRG1 LINC-PINT PSMA7 SH2D2A HCST GUSB SEC61B TAX1BP1 RNF149 GNG5 PIP5K1A SIGIRR ATP6V1H RXYLT1 TRAPPC2B STK4 GPR174 CLEC2B CDC5L MLEC SQSTM1 GNAS DAXX ADAR NRIP1 TPI1 ITM2C NOSIP HADHA GTF2B ATP5IF1 ATP6V0B VEZF1 MYBL1 TOMM5 UQCRFS1 PDE4B ELL MRPL18 TRAC PIGBOS1 PPIB MDH2 PMVK PGAM1 PEX2 RPA2 FBXW5 MICOS13 CYBC1 ELOB PSMB4 COX6C GTF2I CHMP4A ALOX5AP SSR3 SELENOF AIP UXT TBCB SKAP1 RBM4 NDUFA9 DNAJB11 ATP5MK REX1BD GATA3 MVP GIMAP5 DNAJC2 SHKBP1 CAP1 SSBP1 SUZ12 SNHG6 LTB HECA WDR83OS DUT ANXA2 RRAGA PPP1R12A TAPBP DOK2 DYNLT3 RBPJ ATP6V1G1 CD40LG OCIAD1 TCEA1 ATM SLC44A2 OGA HNRNPK PPP2R5E NDUFA4 PLAUR NT5C3A RBL2 PHTF2 ZFAND5 CCR6 BCL3 GYPC SPTY2D1 MBP AKAP9 CD83 HNRNPF RAC1 SSR2 SMARCC2 MRPS21 SRSF3 DDX21 NDUFC1 BST2 FSD1 TRIB2 SF3B6 GDI2 HLTF APBB1IP LY6E NDUFB4 NAA38 FKBP1A PSMB10 FDPS MBNL1 UQCRB TMED2 H1-10 RO60 CEP57 CDKL3 CAPNS1 GIMAP1 EZR HOXB4 JAGN1 EIF2S2 DNAJB9 KCTD20 ARGLU1 EWSR1 SH3GL1 EIF3F RABGGTA MEA1 MLLT3 SARNP UBAC2 TRA2A ARL6IP1 GPI GUK1 DPH3 TGFBR2 ASB2 SRP9 GNAI2 RAB5IF PPIH EIF4A2 UBQLN1 GPR65 RASAL3 NDUFB6 RPN1 HNRNPA3 GBF1 OGG1 LCP2 DESI2 DUSP2 MGST3 CCDC18-AS1 CD53 POLM RTRAF ME2 PHAX ATP6AP1 RDH11 TCERG1 ACTR8 SLC1A5 DSE SLC25A39 FDFT1 CIAO2A LATS1 WDR33 CCDC107 MARCHF7 FAM174C LRPAP1 COG6 IL10RA MNAT1 CLIC3 TUSC2 NAT9 FAM13B EPG5 FKBP11 CREB3 ARHGAP45 MRPL9 MGA MIDEAS PHF20 CHD9 RCE1 VDAC2 EEF1E1 SSB GBP5 NMRK1 CCT8 SCAP ACER3 VAMP8 TALDO1 PHGR1 TC2N GRID2IP EIF2AK1 TOLLIP TBC1D10A C17ORF100 NFRKB CCDC137 E4F1 XRCC5 FCMR PGPEP1 DGCR6L CHURC1 OFD1 MAP4 PLEKHA2 AMD1 MORF4L1 IER5 DCTN2 CTDSP1 DDX6 AHCYL1 PNKD ELOVL1 FXR1 SAPCD1 SELENOT UBE2L6 PMM2 RNF113A PGRMC2 CPSF6 ZNF267 MRPL50 IL16 CREBL2 ASXL2 NUDCD3 ZNF445 C3AR1 CCR5 DGKE RUNX1 AKAP11 RP11-562I5 ARIH1 PPP1CB NDUFAF3 MRPS5 ACTR1B TPD52 SF3A2 CLTC TMEM33 NDUFA5 HMGXB4 PPP1R21 RC3H2 ZNF580 PRKCSH GZMM SEC13 CTNNA1 VPS18 SYNGAP1-AS1 MAPRE1 ZC3H8 DSTN MAN2B2 MGAT4A PAK2 CRY1 LSM7 ZC3H6 TMEM230 CPQ PSMD3 TIMM17A TYMP ST6GALNAC6 PHB2 KDELR2 LAMP2 NUMB RALY ADAM8 R3HDM4 DNAAF2 ADPRM CCSER2 THRAP3 KIDINS220 RBM15B DEF6 ADNP POLR1E IQGAP1 POFUT2 EIF3K NOL7 NMUR1 NOM1 GNPDA1 PKM CWC25 IGF2R ATP2B4 GPS1 LRP10 CA10 ERI3 SMAD3 ANP32B COX7A2L EHBP1 CHUK H2AZ1 LPIN2 ASAH1 MRPL33 BCL2 DNAJC1 USO1 SUMF2 CHCHD3 NAA20 TMEM60 IER3IP1 FLII YBX1 LAIR1 MESD TSC22D1 SF1 TMEM273 PLCG1 NMD3 TMEM18 ABHD14A SLFN5 ENTR1 CHMP6 IRF2BP2 CHCHD5 HTATSF1 RPS10 LSM14A SUPT7L PEX26 YJU2 HDDC3 CFDP1 SELPLG ENO1 KDM5A MTLN ZNF706 ERVK3-1 DCP1A DBNDD2 TAX1BP3 SPINK4 RNF31 NEPRO SHFL PSMD8 IL17RA CHST14 MPC1 GLOD4 BICRAL SMIM26 KIF5C PRKAR1A TENT5C NPRL2 TTC9C RPUSD3 SSH1 ARHGAP35 RSBN1 ECHDC2 G3BP2 PRR5 SMG9 SUPT3H ATP2A3 SIN3B LSM12 PTEN ZNF93 NONO SRRM2 ITFG1 C21ORF91 HINT1 GOT1 C1QTNF1 AZIN1 MZT1 LAP3 NDUFB8 NDUFA13 FRMD4B LYRM2 IL21R PARP12 SCMH1 PBRM1 HAUS4 RAB4B FLJ40194 HIKESHI NR1D1 MFSD8 SRSF1 SMIM30 XRN2 SAMD4B SEM1 CAPZB PIAS4 IKZF1 EIF3I AGTPBP1 MZT2B C1ORF122 MUTYH WDR61 DDX39B CCNDBP1 ZNF816 PFDN2 POLR2F ANO6 NDUFS6 CTNS ATP11B SIVA1 EIF4E3 CTSC SMC4 CDC42SE2 FAM131C PLPP6 TRPC4AP MTMR14 GABARAPL1 FAM133B RFNG EIF2B1 LSM1 ISY1 EIF3G ORAI2 FAM53B SEPTIN2 HTRA2 SEPTIN6 KAT7 MBD4 CAPZA2 WDR20 NDUFB7 TWF2 COMMD5 GPR68 SPPL3 TMEM59 NCL PJA2 SDHA SLMAP CBX3 TMEM161B-AS1 PPOX ACBD3 GOLGA7 GTF2H1 MAP3K2 COA6 PHF5A COPS7A NAA10 SNX14 CUL1 ALG13 TNIK STARD3 LINC00892 SNAI3 RABAC1 USP42 NDUFA3 DDX58 PSMC6 RBCK1 HNRNPH3 ZFAND6 SDHD CHCHD10 PUM1 LDLRAD4 TCHP CNOT11 KIF1C SEC61G TACC1 PSMD6 NIPBL DAPP1 RAB2A TRIM5 LMNB2 SUPT6H AP3S1 CLK1 NSUN2 NUP88 PPA1 UMAD1 SETD9 DCAF15 ANXA2R TMEM9B EGLN2 MRPS18B PSD4 GTDC1 SUCO BUD31 IER2 UPF3A PINX1_ENSG00000254093 NUDC TAF7 UTP15 PTPMT1 PSME2 MALT1 PSTPIP1 TSPYL1 CTSD HIGD2A SBF1 LINC00667 FEM1A RAB5C TAF2 SHOC2 RASL11A AMZ2 TRIM65 ATP6V0E1 HEBP1 PSMD1 NDUFAF8 CORO1B MRPL34 UBN1 RAE1 SPINT2 SOCS1 TRIM52-AS1 TFDP1 TES YIPF2 STX5 BZW1 PDCL3 NOLC1 TPM3 INTS11 RAB35 COQ10B ZNF480 C1D THAP4 EBAG9 FOXP1 SLC30A6 NUS1 MRPS24 PPP2CA USP4 C9ORF16 MSH2 UQCRC1 SLC38A1 ACO2 WASHC5 TXLNG PHF20L1 RAB6A SERINC1 YTHDC1 MIF4GD C1GALT1 KRR1 NDFIP1 GMFG DCAF8 SNX6 PAIP2 PLAA SRP19 RAD21 TECR OTUB1 CLK3 BNIP2 NEK9 COPE RAB9A RMC1 JOSD2 MAP3K1 RNF144B ADGRE5 UBA5 NLRC3 LYPLAL1 SURF6 PDE9A ZNF720 C17ORF107 SPPL2A OPTN LACTB DNAJA2 CAMK4 PLP2 ARMC6 IDH2 FUNDC2 SNHG3 CAPG TMEM106C DR1 PGGHG TUT7 CYB561D1 EIF3E FURIN VPS26B APBB1 NTAN1 ATP5MF MAX MRPL14 ATP5ME ORAI1 SEC62 TNFRSF1A HTT EIF2AK4 SCAPER POLB FAM204A RRP12 ABHD5 IMPDH1 TAF1D TDG IMP4 SMC6 TRIM28 FAS ILK PPP1R16B TSR3 NOP56 EIF5B CUTA GLO1 PPP3CB CENPK MTRR ITGAE GOLGA3 GFER EPSTI1 THEMIS TRIAP1 HSD17B7 TSTD3 ANKRD39 PIP4K2A MTFP1 FAM222B SRFBP1 HOXB2 TRAPPC5 SRSF8 HECTD1 CCAR1 XKR8 RBM26 ISCA1 TRBC2 UBE2B TIMM8B RPUSD1 SAT2 FAM102A ST6GAL1 METTL9 C2ORF68 PERP SNU13 ANKZF1 CBX5 GPN1 COMMD8 EGLN1 HERPUD1 SENP6 SLC38A2 AARS1 GNL2 G3BP1 GPR108 PSMA4 SP100 MFSD10 TMEM70 QRSL1 DAD1 RPS19BP1 SNHG1 CSDE1 TAF4B ASF1A N4BP2L2 RNF138 FBXL5 JAK1 NCLN RAB11B PPP1CA ITGB2 KDELR1 PSMB1 MTX2 SLC35E2A NDUFS8 HAGH PIGW IVNS1ABP SDHC POLDIP2 EIF4G1 ANKRD36 SEPTIN7 CSNK1D PSMF1 TM9SF4 GAB3 MYL6B HNRNPL WIPF1 GATAD1 RFFL PRKCQ-AS1 GTPBP10 INO80D IFITM3 GAS5 WASHC1 CTNNAL1 PTGER2 USP33 ARRB2 ARMCX6 GMIP UMPS TRIM22 DDX41 SFPQ RBM25 MRPL38 CNP FAM193B MAP3K11 WDR74 PIGC CDC37 PPIP5K2 TRMO AGTRAP JMJD8 ARID5A ALKBH7 CYTH1 SET PAF1 SNHG5 FAM174B ADI1 SUDS3 CDCA7 GTSF1 MTCH1 TGIF2 MLX PYGO2 EIF2S1 DNPEP IL23R RPA1 CDC37L1 GYG1 ANP32A SRSF2 RNASEH2B WDR44 ZPR1 GIPC1 RRM2B ILVBL ARL2BP BRK1 YIF1A CEP164 DPY30 NUDT7 PPP2R1A R3HDM1 MSN PSIP1 HMGN4 CCDC152 STOML2 EEF1AKMT2 H2AZ2 CHST12 CHFR FKBP15 SPNS1 APH1A NR4A3 CNOT9 GPSM3 TESK1 CDK5RAP1 TNFAIP1 FAM118B ZBTB7A PCED1B-AS1 MRPS18A BUB3 ICMT DDIT3 DNM1L DCTN4 PPIG CCDC91 LZIC CHD2 BIRC2 AHSA1 ARAP2 COMMD10 NUCKS1 ASAP1 DCTPP1 ETFDH NAA80 STAU2 CCNL1 CASP3 CCDC174 PRNP APOA1-AS PPP1R11 SRP68 C1ORF43 RASSF1 MYD88 AKAP13 SLC25A5 ERH UBE2I ATP5PD ORC3 NUP37 TTC31 WBP2 PDE7A KPNB1 ETFA STK25 PJVK COX19 ATP5MC3 PPP2R2A MDH1 PSMB6 GTF2E2 B3GNT2 TSC22D4 GPD2 MBIP ZFR JTB GPRIN3 LPIN1 SLC35A1 NXT1 KLHL22 DYNC1I2 AP1S1 PPP1R15B SMIM15 TMEM65 HAUS3 KLHL28 MRPL20 MVK ATR MIB2 YPEL3 SCOC WASHC2C SPRY1 LCLAT1 CCDC25 SLC43A3 CIAO3 WBP11 TRIM4 C17ORF49 DTX1 VAMP5 ZSCAN16-AS1 TNFSF13B ARHGAP15 CISH DCP2 GRPEL2 PHB AASDHPPT PUM2 ISG20 PFKM SLC2A1 NBL1 TMED4 COPS3 SRSF11 ZNF710 POMP TTC14 ZNF414 PISD TMEM219 MIR23AHG COQ4 OXA1L DNAJA3 KMT2A MAGOH LYPLA2 WSB1 POLR2B AKIRIN2 PSMB2 UIMC1 DDX18 TCOF1 TNFRSF14 STARD3NL CELF2 NABP1 PTP4A2 LRRC59 TMEM167B CAMK2G SLC4A1AP EIPR1 NUCB2 MED6 VEZT SP4 ARL3 USP12 ARHGAP9 VMAC FIP1L1 RP11-316M20 ADH5 TMC6 IRF1-AS1 LIG4 SNX1 MACF1 GNL1 TPM4 RMND5A LPAR6 DNAJB2 NOP53 CDK2AP1 FIS1 PITPNM1 SF3B4 SNAP47 S100PBP C4ORF33 CHST11 RGS18 C7ORF31 PSMD4 EFCAB7 TTC39C ANXA5 KRI1 FAM214B DUSP12 NR2C1 CBLB UNG LSM8 MYO9B GLMN RALA GTF3C6 ISCA2 DNTTIP2 SELENOK MRPS36 KPNA3 THRA PTS DUSP6 VTA1 PPARG SEPTIN9 UBE2N FRY BCAS2 CAMKMT SAMHD1 GBP2 TMEM50A STAT5A IL17A PCYT2 ISG15 MAPK3 PDCD4-AS1 TIAL1 RTF1 CSNK2B RBM17 LPCAT4 ST13 CNBD2 TMEM184B RNPC3 ARCN1 SMAP2 BLOC1S1 NAMPT ZNF597 EMG1 SLC66A3 HDLBP C11ORF58 SH3BP1 SMARCB1 RP11-726G1 ZNF564 IFI27L2 ATG3 C6ORF226 REL GIMAP6 OLFM2 OGFR BAZ1A RAB7A EIF3L TBC1D20 SDHAF2 PARP10 GAK FYN TBXAS1 PRDM1 FAM241A TMEM263 SRPK1 CD2BP2 SERGEF SURF4 MTERF4 PPP1R10 IRF9 USP14 ZFAND2B AK3 CYTH3 PEBP1 PLIN2 NARF ICAM2 ZRSR2 HELZ C14ORF119 DCAF5 RBM42 ITFG2-AS1_ENSG00000258325 ZNF524 CRNKL1 FASTK UBXN1 MYO1G QTRT1 FLOT1 DNASE2 STK40 USP47 RFLNB ZFYVE28 FHL1 TATDN2 SLC25A25 ATP6V1B2 GSAP MRPL10 PSMD11 ZNF821 RANBP9 TIPRL TMED10 EIF3M HMGN2 UHMK1 TRBV30 EMP3 HPS6 TTC17 OSBPL8 COMT ERP29 GRPEL1 ZNF595 ROGDI RAD23A LYSMD2 ANKRD44 FRMD8 SRSF10 BCL2A1 TBC1D31 ALDH9A1 CHMP1A GABARAPL2 RPS20 MRPS2 CAST TMEM200A H4C2 ATRAID CRYBG1 CRY2 OSBPL7 TRAPPC1 POLR2K TESMIN UBE2D1 GNAI3 SLC39A6 HMGCS1 TMEM138 TMEM223 CHP1 ATP5MJ MSRA SQLE ATRX ZNF207 PPP1R15A DNAJC3 SNX3 FBXO9 FAM162A BTF3L4 NKIRAS1 PRDX6 EHMT1 SYS1 USP36 STX4 TMED7 NR4A1 GNA13 LINC00299 ST3GAL5 IDH3A NAA16 SLC38A10 CRYBB2 B4GALT1 METTL26 MRPL54 TRAF3IP2 DTX3L NDUFA6 NDUFAF4 SUCLA2 CALHM2 GTF2IRD2B RP11-706O15 IK MAGI3 TCTA PBDC1 BRD7 IRF1 P2RY10 CTBS CUX1 PRKD2 PGK1 BCLAF1 IRAG2 CIAPIN1 TNFRSF25 SNRNP200 IL2RG PSMA6 UQCRC2 RTF2 SLC35C2 GNG2 ZBTB22 MEF2A NOP10 LY9 MAP2K2 COMMD1 TMEM203 VPS29 SOD2 CITED2 FLI1 FMNL1 MRTO4 RETREG2 FGFR1OP2 RNF41 UTP4 TKT DHX36 ID2 TMEM245 ERP44 ESF1 BAG3 NDEL1 DAP3 HM13 PTGR2 ZFAND3 UCP2 BIN2 MCF2L2 PHKB SUB1 PDE3B CEP250-AS1 RNH1 WDR1 FAM117A CAMTA2 UGCG LINC00294 PHF14 PTPN22 KATNBL1 NT5DC1 TRIM38 ZNF780A C19ORF53 SNRPG TDP2 EIF4H TMEM140 ZCCHC17 ANKLE2 CDS2 EPHB6 CCDC90B ATAD3C COL18A1 FBP1 AHCY AFTPH ZNF432 HSPA9 CMSS1 FGD3 ANXA7 SLC25A13 RNF5 KRT10 KDM2B-DT ATP5F1D SCAMP3 KIF2A SHMT1 MRPL57 CD5 MLH1 ACOX3 ZNF593 TTI2 TRAPPC10 SRP72 CXCR6 COPS5 TET2 HEBP2 OGT ANKRD13D CEBPZ MRPL23 UBE2V1 AP2M1 SLC39A4 LLGL1 DLD ACAA2 SCAMP4 PARP8 CXXC1 MAD2L2 GIT2 OSGIN2 FAM219A ERLEC1 PTPN7 TMEM41B SERTAD3 TCF7 ARPC5 NCBP2AS2 ZNF211 SNRPB2 B9D2 ANKRD27 TIPARP RAB1B SRM PARP16 EIF4EBP2 GDI1 ARPC5L CEACAM21 IVD EXOSC4 KDM6A STARD7 JAK3 CBR1 ARL6IP5 STK26 UFM1 ARHGAP18 ESD ISOC2 HCFC1 RING1 PRELID1 MGME1 NDUFC2 CELF1 PNP IFT27 KBTBD2 TUFM TSTD1 CISD3 C17ORF67 BUD23 SCRN2 CHCHD7 FARSA RP11-29H23-1 LINC02481 TPP1 THYN1 ARHGEF1 GCLM PRPF39 TRAPPC6B SLF1 SMIM3 EVI2A AZIN2 ASH1L API5 COG2 SLC25A22 CHD4 WRAP73 CDC26 UBE2L3 EPC1 ATP2B1 CCAR2 RRN3 UBA1 CSNK1A1 CASP8 PELI1 CNOT2 SRP54 VAPA MRPS33 NDUFB11 NAF1 UBE2G2 DVL3 TLE5 CALCOCO2 UQCR10 SIRPG COX7B NDRG1 EIF2S3 H3C4 SMCHD1 RAN MAPKAPK5-AS1 TSG101 HAX1 ANKDD1A DCTN3 CD55 PSKH1 PET100 IMMT SRGAP2C H3-3A PIGH RBM48 FAM177A1 TEFM FBXL4 MID1IP1 PRR5L SLC25A11 NGDN NCAPD3 SLC9A3R1 CDIP1 GRK6 SDF4 DENND2D CASP7 EFR3A NFAT5 PRELID3B TEX30 CIRBP CSNK1G1 MCRS1 CHMP2B TMOD3 PRKX POLR2C NSFL1C POLR1H TAP2 PIANP ARL6IP6 RIPK1 TDP1 SCAF11 SNRPN ZNF335 USP3 UBQLN2 ARMCX3 HSDL1 RSU1 TXNL1 TMEM101 BAD KIF5B AP1AR FAM104A GBP4 AC058791 NDUFB1 DPP7 NDUFB2 RANGAP1 SETX MAF1 NME4 TRGV10 TACC3 AP5Z1 PITPNB EDEM1 CTD-2095E4 ANKRD28 MINDY2 YPEL4 IGHV4-34 TRIM44 ARL6IP4 GART RAD18 SF3B5 BZW2 ECD ZYX HECW2-AS1 CNTRL RECQL PARP9 UPP1 PYCR2 ARHGAP30 TBC1D22B RASGRP1 STIM1 ZNF263 ZNF230 LDLRAP1 PDCD2 FOXO1 UNC119B RNF114 YPEL5 IFNGR2 PRRC1 EIF4B PDXK RAB8B RICTOR ARL8A ESCO1 RBM38 PPIF GMPS HDDC2 FBXO42 PTP4A3 SERPINH1 TRIM69 WTAP PEMT VPS72 WAS PPP4R3A CDV3 CETN2 VAC14 TRIM21 DAZAP2 ARF5',\n",
       " 'cell_type': 'CD4-positive helper T cell',\n",
       " 'tissue': 'ileum',\n",
       " 'batch_condition': 'A29',\n",
       " 'organism': 'Homo sapiens',\n",
       " 'sex': 'female'}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_idx = 0\n",
    "arrow_ds[sample_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Custom Prompt Formatting\n",
    "\n",
    "Here, we will define a custom prompt template for a new task which we will finetune a Cell2Sentence model on. For the purposes of this tutorial, we will define a task where the Cell2Sentence model is given a cell sentence and the tissue which a cell originates from, and the model must predict the cell type of the cell. This is a variation of the standard cell type prediction task supported by Cell2Sentence, but it demonstrates that we can modify the input prompt of Cell2Sentence to include new information and rearrange the input format for new tasks.\n",
    "\n",
    "The keywords enclosed in curly braces `{}` will be replaced with the actual values when the prompt template is applied to a cell sentence sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "custom_input_prompt_template = \"\"\"Given below is a list of {num_genes} gene names ordered by descending expression level in a {organism} cell. Given this expression representation as well as the tissue which this cell originates from, your task is to give the cell type which this cell belongs to.\\nTissue type: {tissue_type}\\nCell sentence: {cell_sentence}.\\nThe cell type corresponding to these genes is:\"\"\"\n",
    "answer_template = \"{cell_type}\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we create a subclass of the PromptFormatter class. The PromptFormatter class is responsible for formatting each cell sentence sample in our dataset with a prompt template. It defines a function called `format_hf_ds` that takes in a cell sentence arrow dataset and returns a formatted dataset, applying the prompt template to each sample. We will implement our custom formatting logic in this `format_hf_ds` function.\n",
    "\n",
    "You can see the original PromptFormatter class [here](https://github.com/vandijklab/cell2sentence/blob/custom_formatter/src/cell2sentence/prompt_formatter.py)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CustomPromptFormatter(PromptFormatter):\n",
    "    def __init__(self, task_name, input_prompt, answer_template, top_k_genes):\n",
    "        super().__init__()\n",
    "        self.task_name = task_name\n",
    "        self.input_prompt = input_prompt\n",
    "        self.answer_template = answer_template\n",
    "        self.top_k_genes = top_k_genes\n",
    "        assert top_k_genes > 0, \"'top_k_genes' must be an integer > 0\"\n",
    "\n",
    "    def format_hf_ds(self, hf_ds):\n",
    "        \"\"\"\n",
    "        Our custom formatting function which will loop through the dataset samples and format\n",
    "        cell sentences and cell metadata into the custom prompt template.\n",
    "\n",
    "        Arguments:\n",
    "            hf_ds: Huggingface arrow dataset containing cell sentences to format prompts with.\n",
    "        \n",
    "        Returns:\n",
    "            ds: Huggingface dataset containing formatted cell sentences and cell metadata.\n",
    "                Dataset columns expected: 'sample_type', 'model_input', 'response'\n",
    "        \"\"\"\n",
    "        # Initialize lists to store formatted model inputs and responses\n",
    "        model_inputs_list = []\n",
    "        responses_list = []\n",
    "\n",
    "        # Loop through each sample in the dataset\n",
    "        for cell_idx in range(hf_ds.num_rows):\n",
    "            # Get the sample from the arrow dataset - contains cell sentence and cell metadata\n",
    "            sample = hf_ds[cell_idx]\n",
    "            \n",
    "            # Get cell sentence\n",
    "            single_cell_sentence_str, num_genes_str = get_cell_sentence_str(sample, num_genes=self.top_k_genes)\n",
    "            model_input_str = self.input_prompt.format(\n",
    "                cell_sentence=single_cell_sentence_str,\n",
    "                num_genes=num_genes_str,\n",
    "                organism=sample[\"organism\"],\n",
    "                tissue_type=sample[\"tissue\"],\n",
    "            )\n",
    "            response_str = self.answer_template.format(\n",
    "                cell_type=sample[\"cell_type\"],\n",
    "            )\n",
    "\n",
    "            model_inputs_list.append(model_input_str)\n",
    "            responses_list.append(response_str)\n",
    "\n",
    "        # Create formatted Huggingface dataset\n",
    "        ds_split_dict = {\n",
    "            \"sample_type\": [self.task_name] * hf_ds.num_rows,\n",
    "            \"model_input\": model_inputs_list,\n",
    "            \"response\": responses_list,\n",
    "        }\n",
    "        ds = Dataset.from_dict(ds_split_dict)\n",
    "        return ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we define an instance of our CustomPromptFormatter class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.CustomPromptFormatter at 0x1547b4edda90>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task_name = \"cell_type_pred_given_tissue\"\n",
    "prompt_formatter = CustomPromptFormatter(\n",
    "    task_name=task_name,\n",
    "    input_prompt=custom_input_prompt_template,\n",
    "    answer_template=answer_template,\n",
    "    top_k_genes=100\n",
    ")\n",
    "prompt_formatter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's format a few dataset samples with our custom prompt template to see what the formatted dataset looks like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['cell_name', 'cell_sentence', 'cell_type', 'tissue', 'batch_condition', 'organism', 'sex'],\n",
       "    num_rows: 10\n",
       "})"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small_example_ds = arrow_ds.select(range(10))\n",
    "small_example_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['sample_type', 'model_input', 'response'],\n",
       "    num_rows: 10\n",
       "})"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formatted_small_example_ds = prompt_formatter.format_hf_ds(small_example_ds)\n",
    "formatted_small_example_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'sample_type': 'cell_type_pred_given_tissue',\n",
       " 'model_input': 'Given below is a list of 100 gene names ordered by descending expression level in a Homo sapiens cell. Given this expression representation as well as the tissue which this cell originates from, your task is to give the cell type which this cell belongs to.\\nTissue type: ileum\\nCell sentence: RPLP1 ACTB EEF1A1 HSP90AA1 TMSB4X B2M FTH1 KLF6 HSPA1B MALAT1 RPS12 HSPA8 RPL13 MT-CO1 ATF3 MT-CO2 RPL41 TPT1 MT-CO3 RPS19 HLA-B RPL10 RPS4X RPL28 MT-CYB DUSP1 RPL30 MT-ND4L RPS15 FOS RPL34 RPS2 RPLP2 MT-ND3 RPS18 RPS8 TRBV7-2 RPL32 RPS3 ANXA1 RPL11 HLA-C RPS27 ACTG1 UBC RPL3 RPL37 RPLP0 MT-ATP6 JUNB RPS28 RPL18 UBB MT-ATP8 RPS14 RPL39 PFN1 GAPDH HSPA1A RPL18A SRGN RPS27A RPL26 RPL19 RPS15A HLA-A DNAJB1 RPS3A CREM RPS13 MT-ND1 RPL21 RPS25 BTG2 RPL35A FAU RPL8 RPL7A RPS24 RPS6 RPS16 RACK1 NFKBIA RGS1 RPL29 CALM1 RPL9 RPL37A MT-ND5 TNFAIP3 RPS23 IL7R RPL36A PTMA NFKBIZ UBA52 EIF1 CRIP1 CORO1A RPL14.\\nThe cell type corresponding to these genes is:',\n",
       " 'response': 'CD4-positive helper T cell'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "formatted_small_example_ds[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#----Model input:----#\n",
      "Given below is a list of 100 gene names ordered by descending expression level in a Homo sapiens cell. Given this expression representation as well as the tissue which this cell originates from, your task is to give the cell type which this cell belongs to.\n",
      "Tissue type: ileum\n",
      "Cell sentence: RPLP1 ACTB EEF1A1 HSP90AA1 TMSB4X B2M FTH1 KLF6 HSPA1B MALAT1 RPS12 HSPA8 RPL13 MT-CO1 ATF3 MT-CO2 RPL41 TPT1 MT-CO3 RPS19 HLA-B RPL10 RPS4X RPL28 MT-CYB DUSP1 RPL30 MT-ND4L RPS15 FOS RPL34 RPS2 RPLP2 MT-ND3 RPS18 RPS8 TRBV7-2 RPL32 RPS3 ANXA1 RPL11 HLA-C RPS27 ACTG1 UBC RPL3 RPL37 RPLP0 MT-ATP6 JUNB RPS28 RPL18 UBB MT-ATP8 RPS14 RPL39 PFN1 GAPDH HSPA1A RPL18A SRGN RPS27A RPL26 RPL19 RPS15A HLA-A DNAJB1 RPS3A CREM RPS13 MT-ND1 RPL21 RPS25 BTG2 RPL35A FAU RPL8 RPL7A RPS24 RPS6 RPS16 RACK1 NFKBIA RGS1 RPL29 CALM1 RPL9 RPL37A MT-ND5 TNFAIP3 RPS23 IL7R RPL36A PTMA NFKBIZ UBA52 EIF1 CRIP1 CORO1A RPL14.\n",
      "The cell type corresponding to these genes is: \n",
      "\n",
      "#----Response:----#\n",
      "CD4-positive helper T cell\n"
     ]
    }
   ],
   "source": [
    "print(\"#----Model input:----#\")\n",
    "print(formatted_small_example_ds[0][\"model_input\"], \"\\n\")\n",
    "print(\"#----Response:----#\")\n",
    "print(formatted_small_example_ds[0][\"response\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the model input is formatted with the custom prompt template, and the response is the cell type annotation for the cell sentence sample. One thing to note here is that in the current format, all samples will have the exact same input template, so there will not be variations in natural language of how we ask the LLM to perform the task. It can be beneficial to provide variations of prompt templates to provide some diversity - simply create several templates and choose one when formatting each sample in the formatting function!\n",
    "\n",
    "For our finetuning run, the csmodel finetune() function will do the formatting on the full dataset for us, so we do not need to do it manually. We will simply pass in our arrow dataset as well as our CustomPromptFormatter instance to the finetune() function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create CSData object\n",
    "\n",
    "As one last data step, we need to create a CSData object which will wrap around our arrow dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "c2s_save_dir = \"/home/ap2853/scratch/c2s_api_testing\"  # C2S dataset will be saved into this directory\n",
    "c2s_save_name = \"dominguez_immune_tissue_tutorial7\"  # This will be the name of our C2S dataset on disk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f9840ca44f584b0393ff865e806c20ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Saving the dataset (0/1 shards):   0%|          | 0/29773 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "csdata = cs.CSData.csdata_from_arrow(\n",
    "    arrow_dataset=arrow_ds, \n",
    "    vocabulary=vocabulary,\n",
    "    save_dir=c2s_save_dir,\n",
    "    save_name=c2s_save_name,\n",
    "    dataset_backend=\"arrow\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CSData Object; Path=/home/ap2853/scratch/c2s_api_testing/dominguez_immune_tissue_tutorial7, Format=arrow\n"
     ]
    }
   ],
   "source": [
    "print(csdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Cell2Sentence Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we will load a C2S model which will finetune on a new dataset, as in tutorial 3. This model can be a LLM pretrained on natural language, or it can be a trained C2S model which will undergo further finetuning on a new dataset of interest. Typically, starting from a pretrained C2S model benefits performance, since C2S models were initialized from natural language-pretrained LLMs and trained on many single-cell datasets on different tasks.\n",
    "\n",
    "For this tutorial, we will start finetuning from the C2S-Pythia-410M cell type prediction model, which was trained to do cell type prediction on many datasets from CellxGene and Human Cell Atlas. More details about the C2S-Pythia-410M cell type prediction model can be found in the Model Zoo section of the ReadME in the GitHub repo, or in the Huggingface model card.\n",
    "\n",
    "We can define our CSModel object with our pretrained cell type prediction model as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    }
   ],
   "source": [
    "# Define CSModel object\n",
    "model_name_or_path = \"vandijklab/C2S-Pythia-410m-diverse-single-and-multi-cell-tasks\"\n",
    "save_dir = \"/home/ap2853/scratch/c2s_api_testing\"\n",
    "save_name = \"cell_type_pred_pythia_410M_2\"\n",
    "csmodel = cs.CSModel(\n",
    "    model_name_or_path=model_name_or_path,\n",
    "    save_dir=save_dir,\n",
    "    save_name=save_name\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CSModel Object; Path=/home/ap2853/scratch/c2s_api_testing/cell_type_pred_pythia_410M_2\n"
     ]
    }
   ],
   "source": [
    "print(csmodel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finetune on new dataset\n",
    "\n",
    "Now, we will finetune our C2S model on our custom task. The finetune() function will format the full dataset for us using our CustomPromptFormatter instance, so we just need to pass in our custom prompt formatter to the finetune() function.\n",
    "\n",
    "For training, we will need to define training arguments for finetuning our C2S model on our new dataset. Huggingface's Trainer class is used to do training, so we can utilize different training techniques (e.g. mixed precision training, gradient accumulation, gradient checkpointing, etc.) by specifying the corresponding option in the TrainingArguments object. This gives us a vast array of possible options for training, and will allow us to specify important parameters such as batch size, learning rate, and learning rate schedulers. See the full documentation for training arguments at:\n",
    "- https://huggingface.co/docs/transformers/en/main_classes/trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cell_type_pred_given_tissue'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "task_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ap2853/scratch/c2s_api_testing/2024-12-07-18_50_20_finetune_cell_type_pred_given_tissue\n"
     ]
    }
   ],
   "source": [
    "datetimestamp = datetime.now().strftime('%Y-%m-%d-%H_%M_%S')\n",
    "output_dir = os.path.join(csmodel.save_dir, datetimestamp + f\"_finetune_{task_name}\")\n",
    "if not os.path.exists(output_dir):\n",
    "    os.mkdir(output_dir)\n",
    "print(output_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we define our training arguments. For this tutorial, we will use a batch size of 8 with 4 gradient accumulation steps, yielding an effective batch size of 32. We will use a learning rate of 1e-5 with a cosine annealing scheduler, and we will train for 5 epochs total. Some other important parameters specified here are:\n",
    "- bf16: Uses mixed-precision training with bfloat16 dtype\n",
    "- logging_steps: controls how often we log training loss\n",
    "- eval_steps: controls how often we run the eval loop\n",
    "- warmup_ratio: percentage of training in which learning rate warms up to the base learning rate specified\n",
    "\n",
    "Full explanations of all possible training arguments can be found in the Huggingface Trainer documentation: \n",
    "\n",
    "https://huggingface.co/docs/transformers/v4.44.2/en/main_classes/trainer#transformers.TrainingArguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/gpfs/radev/home/ap2853/.conda/envs/scFB2/lib/python3.8/site-packages/transformers/training_args.py:1559: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n"
     ]
    }
   ],
   "source": [
    "train_args = TrainingArguments(\n",
    "    bf16=True,\n",
    "    fp16=False,\n",
    "    per_device_train_batch_size=2,\n",
    "    per_device_eval_batch_size=2,\n",
    "    gradient_accumulation_steps=4,\n",
    "    gradient_checkpointing=False,\n",
    "    learning_rate=1e-5,\n",
    "    load_best_model_at_end=True,\n",
    "    logging_steps=50,\n",
    "    logging_strategy=\"steps\",\n",
    "    lr_scheduler_type=\"cosine\",\n",
    "    num_train_epochs=5, \n",
    "    eval_steps=50,\n",
    "    evaluation_strategy=\"steps\",\n",
    "    save_steps=100,\n",
    "    save_strategy=\"steps\",\n",
    "    save_total_limit=3,\n",
    "    warmup_ratio=0.05,\n",
    "    output_dir=output_dir,\n",
    "    max_steps=1000  # Set a maximum number of steps - shortened to 1k steps for tutorial purposes\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reloading model from path on disk: /home/ap2853/scratch/c2s_api_testing/cell_type_pred_pythia_410M_2\n"
     ]
    },
    {
     "data": {
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       "model_id": "2a04a9089d0e4801a9499d39e4376a50",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map (num_proc=3):   0%|          | 0/29773 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting training. Output directory: /home/ap2853/scratch/c2s_api_testing/2024-12-07-18_50_20_finetune_cell_type_pred_given_tissue\n",
      "Selecting 500 samples of eval dataset to shorten validation loop.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/gpfs/radev/home/ap2853/.conda/envs/scFB2/lib/python3.8/site-packages/cell2sentence/csmodel.py:203: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
      "Detected kernel version 4.18.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\n",
      "max_steps is given, it will override any value given in num_train_epochs\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33map2853\u001b[0m (\u001b[33maakashdp\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.18.5"
      ],
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       "<IPython.core.display.HTML object>"
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    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>/gpfs/radev/home/ap2853/cell2sentence/tutorials/wandb/run-20241207_185100-xu1hxm6b</code>"
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     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/aakashdp/huggingface/runs/xu1hxm6b' target=\"_blank\">/home/ap2853/scratch/c2s_api_testing/2024-12-07-18_50_20_finetune_cell_type_pred_given_tissue</a></strong> to <a href='https://wandb.ai/aakashdp/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ],
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/aakashdp/huggingface' target=\"_blank\">https://wandb.ai/aakashdp/huggingface</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/aakashdp/huggingface/runs/xu1hxm6b' target=\"_blank\">https://wandb.ai/aakashdp/huggingface/runs/xu1hxm6b</a>"
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1000' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1000/1000 11:55, Epoch 0/1]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>50</td>\n",
       "      <td>4.573200</td>\n",
       "      <td>1.050461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>4.180600</td>\n",
       "      <td>1.039572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>4.216100</td>\n",
       "      <td>1.037922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>200</td>\n",
       "      <td>4.142600</td>\n",
       "      <td>1.032912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>250</td>\n",
       "      <td>4.148200</td>\n",
       "      <td>1.028711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>300</td>\n",
       "      <td>4.068100</td>\n",
       "      <td>1.021555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>350</td>\n",
       "      <td>4.058700</td>\n",
       "      <td>1.018363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>400</td>\n",
       "      <td>4.055900</td>\n",
       "      <td>1.015629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>450</td>\n",
       "      <td>3.984900</td>\n",
       "      <td>1.011592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>4.087400</td>\n",
       "      <td>1.012852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>550</td>\n",
       "      <td>4.063500</td>\n",
       "      <td>1.005052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>600</td>\n",
       "      <td>3.977900</td>\n",
       "      <td>0.997025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>650</td>\n",
       "      <td>4.009300</td>\n",
       "      <td>0.994933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>700</td>\n",
       "      <td>3.966300</td>\n",
       "      <td>0.991463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>750</td>\n",
       "      <td>3.948300</td>\n",
       "      <td>0.988624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>800</td>\n",
       "      <td>4.051800</td>\n",
       "      <td>0.985243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>850</td>\n",
       "      <td>3.922400</td>\n",
       "      <td>0.984506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>900</td>\n",
       "      <td>3.986900</td>\n",
       "      <td>0.981624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>950</td>\n",
       "      <td>3.955200</td>\n",
       "      <td>0.980832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1000</td>\n",
       "      <td>3.937900</td>\n",
       "      <td>0.980721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finetuning completed. Updated model saved to disk at: /home/ap2853/scratch/c2s_api_testing/2024-12-07-18_50_20_finetune_cell_type_pred_given_tissue\n"
     ]
    }
   ],
   "source": [
    "csmodel.fine_tune(\n",
    "    csdata=csdata,\n",
    "    task=task_name,\n",
    "    train_args=train_args,\n",
    "    loss_on_response_only=False,\n",
    "    top_k_genes=100,\n",
    "    max_eval_samples=500,\n",
    "    prompt_formatter=prompt_formatter  # Pass in our custom prompt formatter\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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   "execution_count": null,
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   "execution_count": null,
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   "execution_count": null,
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   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
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